Dimensionality reduction for complex models via Bayesian compressive sensing
Uncertainty quantification in complex physical models is often challenged by the
computational expense of these models. One often needs to operate under the assumption …
computational expense of these models. One often needs to operate under the assumption …
[图书][B] Advanced reduced order methods and applications in computational fluid dynamics
Reduced order modeling is an important and fast-growing research field in computational
science and engineering, motivated by several reasons, of which we mention just a few …
science and engineering, motivated by several reasons, of which we mention just a few …
Comparison of Overwing and Underwing Nacelle Aeropropulsion Optimization for Subsonic Transport Aircraft
This research compares a forward-mounted overwing nacelle configuration to a
conventional underwing nacelle for a single-aisle transport aircraft. We focus on …
conventional underwing nacelle for a single-aisle transport aircraft. We focus on …
A multi-fidelity approximation of the active subspace method for surrogate models with high-dimensional inputs
View Video Presentation: https://doi. org/10.2514/6.2022-3488. vid Modern design problems
routinely involve high-dimensional inputs and the active subspace has been recognized as …
routinely involve high-dimensional inputs and the active subspace has been recognized as …
Assessing the performance of Leja and Clenshaw-Curtis collocation for computational electromagnetics with random input data
D Loukrezis, U Römer… - International Journal for …, 2019 - dl.begellhouse.com
We consider the problem of quantifying uncertainty regarding the output of an
electromagnetic field problem, in the presence of a large number of uncertain input …
electromagnetic field problem, in the presence of a large number of uncertain input …
Stochastic multiobjective optimization on a budget: Application to multipass wire drawing with quantified uncertainties
Design optimization of engineering systems with multiple competing objectives is a
painstakingly tedious process especially when the objective functions are expensive-to …
painstakingly tedious process especially when the objective functions are expensive-to …
High-dimensional multidisciplinary design optimization for aircraft eco-design/Optimisation multi-disciplinaire en grande dimension pour l'\'eco-conception avion en …
S Paul - arXiv preprint arXiv:2402.04711, 2024 - arxiv.org
Résumé D e nos jours, un intérêt significatif et croissant pour améliorer les processus de
conception de véhicules s' observe dans le domaine de l'optimisation multidisciplinaire …
conception de véhicules s' observe dans le domaine de l'optimisation multidisciplinaire …
Pro-ML IDeAS: A probabilistic framework for explicit inverse design using invertible neural network
View Video Presentation: https://doi. org/10.2514/6.2021-0465. vid An inverse design
process has the potential to positively impact the difficulties of the traditional iterative …
process has the potential to positively impact the difficulties of the traditional iterative …
Design space reduction using multi-fidelity model-based active subspaces
View Video Presentation: https://doi. org/10.2514/6.2023-3592. vid The parameterization of
aerodynamic design shapes often results in high-dimensional design spaces, creating …
aerodynamic design shapes often results in high-dimensional design spaces, creating …
A gradient-based sampling approach for dimension reduction of partial differential equations with stochastic coefficients
M Stoyanov, CG Webster - International Journal for Uncertainty …, 2015 - dl.begellhouse.com
We develop a projection-based dimension reduction approach for partial differential
equations with high-dimensional stochastic coefficients. This technique uses samples of the …
equations with high-dimensional stochastic coefficients. This technique uses samples of the …